Meet the Fellow: Tim G. J. Rudner

This entree is a part of our Meet the Fellow blog series, which introduces and highlights Faculty Fellows who have recently joined CDS.

Meet CDS Faculty Fellow Tim G. J. Rudner, who will join us this fall. Tim is a DPhil Candidate in the Department of Computer Science at the University of Oxford where he is advised by Yarin Gal and Yee Whye Teh. He is supported by a Rhodes Scholarship and a Qualcomm Innovation Fellowship. His research aims to develop methods and theoretical insights that enable the safe deployment of machine learning systems in safety-critical settings by drawing on tools from probabilistic machine learning, optimization, and reinforcement learning.

Tim received an undergraduate degree in mathematics and economics from Yale University and a master’s degree in statistics from the University of Oxford. During his DPhil, he was a visiting researcher at UC Berkeley, where he was hosted by Sergey Levine, and at Yale University, where he was hosted by Sekhar Tatikonda. At Oxford, Tim served as an Equality, Diversity & Inclusion Fellow and is a Lecturer for the machine learning graduate course “Data, Estimation & Inference.”

In addition to his technical research, Tim is also engaged in machine learning policy work. He is an AI Fellow at Georgetown University’s Center for Security & Emerging Technology, where his work helps inform policy discussions on the reliability and safety of modern machine learning systems, and an Invited Expert at the OECD’s AI Observatory and The Brooking Institution’s Forum for Cooperation on AI.

Tim’s most recent work has explored the use of variational inference with informative priors over functions to enable reliable machine learning. Examples of this work are his papers “Continual Learning via Sequential Function-Space Variational Inference,” published at the International Conference on Machine Learning, and “Outcome-Driven Reinforcement Learning via Variational Inference,” published at the Conference on Neural Information Processing Systems.

“NYU is one of the world’s premier institutions for both theoretical and applied machine learning research,” said Tim. “I look forward to joining NYU and to collaborating with CDS faculty and students to make modern machine learning methods more reliable!”

To view all our current faculty fellows, please visit the CDS Faculty Fellow page on our website.

By Meryl Phair